Abstract—EEG signals aids in diagnosing various wave
signals recorded by the activities of the brain. It also produces
unavoidable artifacts, in the recording process. The purpose of
this study therefore is to detect ictal and artefact signals, with
the aim of reducing interpretation errors especially those
related to the muscle which are quite difficult to distinguish.
The data used are EEG signal recording results obtained from
Rumah Sakit Universitas Airlangga. It consisted of two classes,
namely ictal and muscle artefact. The signal decomposition
method used is a wavelet transform, known as DWT. While the
extraction feature utilized, consist of quartile, maximum,
minimum, mean and standard deviation. This study also
utilized the SVM with linear, polynomial, RBF and ELM
(ESVM) kernels. Research results shows that the ESVM
classification time is faster than the SVM and other kernels.
However, the values of accuracy, sensitivity, specificity and
AUC are not better.
Index Terms—ESVM, SVM, wavelet transform, ICTAL,
muscle artifact, epilepsy.
Baiq Siska Febriani Astuti, Santi Wulan Purnami, R. Mohamad Atok, and
Diah Puspito Wulandari are with the Institut Teknologi Sepuluh Nopember,
Indonesia (e-mail: baiqsiskafebriani@gmail.com, santi_wp@its.ac.id,
moh_atok@statistics.its.ac.id, diah@te.its.ac.id).
Wardah Rahmatul Islamiyah is with the University of Airlangga, Indonesia
(e-mail: wri1905@gmail.com).
Anda Iviana Juniani is with the Shipbuilding Institute of Polytechnic,
Indonesia (e-mail: anda.iviana@ppns.ac.id).
Cite: Baiq Siska Febriani Astuti, Santi Wulan Purnami, R. Mohamad Atok, Wardah Rahmatul Islamiyah, Diah Puspito Wulandari, and Anda Iviana Juniani, "Classify Epileptic EEG Signals Using Extreme Support Vector Machine for Ictal and Muscle Artifact Detection," International Journal of Machine Learning and Computing vol. 11, no. 1, pp. 170-175, 2021.
Copyright © 2021 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).